[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127863-en":3,"doc-seo-127863-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127863,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction","Predicting credit default risk is critical for financial institutions to reduce losses and maintain profitability and stability. Despite prior use of machine learning on large, complex datasets, identifying effective modeling approaches remains challenging, especially under data imbalance. A comparative study evaluates random forest, decision tree, SVM, XGBoost, AdaBoost, and a multi-layer perceptron on LendingClub loan data, using XGBoost for framework testing.","Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction  \nAKINJOLE, Abisola, SHOBAYO, Olamilekan \u003C [http://orcid.org/0000-0001-](http://orcid.org/0000-0001-)[ ](http://orcid.org/0000-0001-)[5889-7082](5889-7082)>, POPOOLA, Jumoke, OKOYEIGBO, Obinna and OGUNLEYE, Bayode  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/34408/](https://shura.shu.ac.uk/34408/)  \nThis document is the Published Version [VoR]  \nCitation:  \nAKINJOLE, Abisola, SHOBAYO, Olamilekan, POPOOLA, Jumoke, OKOYEIGBO, Obinna and OGUNLEYE, Bayode (2024) . Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction. Mathematics, 12 (21): 3423. [Article]  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nArticle  \nEnsemble-Based Machine Learning Algorithm for Loan Default Risk Prediction  \nAbisola Akinjole 1, Olamilekan Shobayo 1,*, Jumoke Popoola 1, Obinna Okoyeigbo 2 and Bayode Ogunleye 3  \nCitation: Akinjole, A.; Shobayo, O.; Popoola, J.; Okoyeigbo, O.; Ogunleye, B. Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction.  \nMathematics 2024, 12, 3423. [https://](https://)[ ](https://)[doi.org/10.3390/math12213423](doi.org/10.3390/math12213423)  \n[Academic Editors: Raymond Lee](Academic Editors: Raymond Lee), Xinan Yang and Dong Li  \nReceived: 10 September 2024  \nRevised: 3 October 2024  \nAccepted: 30 October 2024  \nPublished: 31 October 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/license](https://creativecommons.org/license)[s/by/4.0/](s/by/4.0/)) .  \n1 School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield S1 2NU, UK; [abisola.j.akinjole@student.shu.ac.uk](abisola.j.akinjole@student.shu.ac.uk) (A.A.); [j.popoola@shu.ac.uk](j.popoola@shu.ac.uk) (J.P.)  \n2 Department of Engineering, Edge Hill University, Ormskirk L39 4QP, UK; [obinna.okoyeigbo@edgehill.ac.uk](obinna.okoyeigbo@edgehill.ac.uk)  \n[3](3 Department of Computing & Mathematics)[ Department of Computing & Mathematics](3 Department of Computing & Mathematics), [University of Brighton](University of Brighton), [Brighton BN2 4GJ](Brighton BN2 4GJ), [UK](UK); [b.ogunleye@brighton.ac.uk](b.ogunleye@brighton.ac.uk)  \n* [Correspondence: o.shobayo@shu.ac.uk](Correspondence: o.shobayo@shu.ac.uk)  \nAbstract: Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques for the model development process, including the technique to address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest, decision tree, SVMs (Support Vector Machines), XGBoost (Extreme Gradient Boosting), ADABoost (Adaptive Boosting) and the multi-layered perceptron, to predict credit defaults using loan data from LendingClub. Additionally, XGBoost was used as a framework for testing and evaluating various techniques. Moreover, we applied this XGBoost framework to handle the issue of class imbalance observed, by testing various resampling methods such as Random Over-Sampling (ROS), the Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Under-Sampling (RUS), and hybrid approaches like the SMOTE with Tomek Links and the SMOTE with Edited Nearest Neighbours (SMOTE + ENNs) . The results showed that balanced datasets sig","cbCaibCoXKn68vdT","https://ap.wps.com/l/cbCaibCoXKn68vdT","pdf",2715100,1,33,"English","en",105,"# Abstract\n# Introduction\n## Problem background and motivation\n## Role of machine learning for credit default risk\n# Methods and comparative models\n## Model set and evaluation framework\n## Data imbalance handling with resampling methods\n# Results and discussion\n## Performance on balanced vs imbalanced datasets\n## Best-performing approach and reported metrics\n# Conclusion\n## Implications for lending platforms","[{\"question\":\"Why is credit default risk prediction important?\",\"answer\":\"It helps lenders avoid large financial losses and maintain trust by estimating the likelihood that borrowers will fail to meet loan obligations.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The study compares random forest, decision tree, SVM, XGBoost, AdaBoost, and a multi-layered perceptron.\"},{\"question\":\"How does the research address data imbalance?\",\"answer\":\"It applies XGBoost as a framework and evaluates multiple resampling methods, including ROS, SMOTE, ADASYN, RUS, and hybrid combinations like SMOTE + Tomek Links and SMOTE + ENNs.\"}]","Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction | 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